Hugo Cisneros

tags Continual learning, Catastrophic forgetting, Distillation, In-context learning, Large language models source (Shenfeld et al. 2026) Summary This paper introduces Self-Distillation Fine-Tuning (SDFT), an on-policy alternative to supervised fine-tuning…

tags Reinforcement learning, Reinforcement learning with human feedback, Reward shaping Recovering an unknown reward function from expert demonstrations such that the demonstrated behavior is optimal under it.

tags Distillation, Supervised fine-tuning, Large language models Distillation regime where the student is trained on a fixed dataset of trajectories generated by a different distribution. typically the teacher, an earlier checkpoint, or a static expert co…

tags Distillation, Reinforcement learning, Large language models, Continual learning Distillation regime where the student samples its own trajectories and minimizes divergence to a teacher evaluated on those samples.

tags Reinforcement learning, Distillation, Large language models, In-context learning source (Hübotter et al. 2026) Summary This paper introduces Self-Distillation Policy Optimization (SDPO), a new algorithm for post-training large language models with re…

tags LLM, Vision transformer Vision language models (VLMs) are generative AI models trained on both text and images.

tags Vision Language Models, Grounding, Synthetic training data, Spatial Reasoning, Foundation models source (Clark et al. 2026) Summary Molmo2 is a family of fully open Vision Language Models (4B, 8B built on Qwen3, and a 7B built on OLMo) trained without

tags Vision Language Models, Reinforcement learning, GRPO, Tool calling, Grounding, Reinforcement learning with verifiable rewards source (Zheng et al. 2025) Summary DeepEyes is a Vision-Language Model that learns to “think with images” — interleaving tex…

tags Large language models, Foundation models, Catastrophic forgetting, Continual learning Off-policy adaptation of a pretrained model by training on (input, target) pairs from expert demonstrations using cross-entropy loss.

tags LLM, Continual learning, Catastrophic forgetting, Transfer learning, Language modeling source (Biderman et al. 2024) Summary This paper provides a rigorous head-to-head comparison of Low-Rank Adaptation (LoRA) against full finetuning of Llama-2-7B on…

tags Vision Language Models, Geospatial AI, Tool calling, GRPO, Reinforcement learning source (Wang et al. 2026) Summary GeoEyes addresses visual question answering (VQA) on ultra-high-resolution (UHR) remote sensing imagery — scenes where task-relevant c…

tags Transformers, LLM, Scaling laws, Attention, Residual neural networks, Memory in neural networks source (Mak, Flanigan 2025) Summary Standard Transformers use a residual stream of dimension \(D\) as a “memory bus” where every layer reads and writes fe…

Notes by Topic ALife 2020 Talk: Alife 2020 keynote Lee Cronin - A Top Down Chemically Embodied Artificial Life Computation (9 links, 1 backlinks) Talk: Alife 2020 keynote Michael Levin - Robot Cancer (4 links, 1 backlinks) Talk: Alife 2020 keynote Luis Za…

tags Object recognition, Vision Language Models, Grounding, Computer vision Object detection where the class vocabulary is unbounded and specified at inference via natural-language phrases or visual exemplars.

tags Computer vision, Image processing, Object recognition, Foundation models Predicting per-pixel labels (instance, semantic, panoptic) for an image, including the open-vocabulary regime where target classes are specified by text or visual exemplars at i…

tags Reinforcement learning, Reinforcement learning with verifiable rewards, GRPO Patterns for designing RL rewards that produce intended behaviors (e.g., conditional bonuses that trigger only on correct outcomes).

tags Reinforcement learning, Reinforcement learning with verifiable rewards, GRPO Pathologies where agents exploit literal reward structure (e.g., spamming tool use without accuracy gains).

tags LLM, Test-time compute, Reinforcement learning, Token-level credit assignment in reasoning traces Prompting and training paradigm where models emit intermediate reasoning steps before a final answer, improving multi-step problem solving and enabling…

tags Machine learning, LLM, Agent, Computer vision, Retrieval augmented generation The application of AI and LLMs to geospatial data like satellite imagery, GIS databases, mapping, and Earth observation.

tags Vision Language Models, Spatial Reasoning, Grounding Task of answering natural-language questions grounded in image content, spanning global scene understanding to fine-grained perception and compositional reasoning

tags Machine learning, Program synthesis, Agent A LLM agent (based on a generic of specialized model) dedicated to programming.

tags Machine learning, Optimization, Meta-learning, Program synthesis source (Lee et al. 2026) Summary This paper introduces Meta-Harness, an outer-loop system for automatically optimizing the “harness” of LLM applications — the code that determines what…

tags Reinforcement learning, Reinforcement learning with verifiable rewards, GRPO, Tool calling RL post-training for LLM/VLM agents that decide when and how to invoke tools during reasoning, with rewards shaped around tool-use policy (necessity, efficienc…

tags Vision Language Models, Tool calling, Spatial Reasoning, Grounding Multimodal reasoning paradigm where VLMs interleave textual chain-of-thought with active visual operations (zoom, crop, search) to acquire evidence on demand.

tags Machine learning, Self-supervised learning Big models trained on a large amount of domain data to learn useful patterns and serve as a basis for other downstream machine learning applications.

tags Transformers, T5, NLP paper (Fedus et al. 2022) Architecture This model increases the parameter count of T5-like architecture while allowing efficient routing through different experts in a mixture of experts.

tags Vision Language Models, Computer vision, CLIP, Contrastive learning, Vision transformer, Foundation models source (Bolya et al. 2025) Summary This paper introduces Perception Encoder (PE), a family of vision encoders from Meta FAIR trained with a pur…

tags Self-supervised learning, Vision transformer, Foundation models, Robotics source (Mur-Labadia et al. 2026) Summary V-JEPA 2.1 is a family of self-supervised video models (ViT-g/G, 1B/2B, plus distilled ViT-L/B variants) from FAIR at Meta that extends…

tags Computer vision, Foundation models, Object recognition, Vision Language Models, Grounding, Synthetic training data source (Carion et al. 2025) Summary SAM 3 (Segment Anything Model 3) is Meta’s third installment of the SAM family of Foundation models…

tags Transformers, Attention, Computer vision, Object recognition, Positional encoding source (Carion et al. 2020) Summary DETR (DEtection TRansformer) reframes object detection as a direct set prediction problem, eliminating hand-designed components that…

tags Neural networks, Transformers Implementation Self-attention is a weighted average of all input elements from a sequence, with a weight proportional to a similarity score between representations.

tags LLM, Diffusion models, Transformers, Test-time compute source (Chen et al. 2026) Summary DFlash is a speculative decoding framework that replaces the usual small autoregressive draft model with a lightweight block diffusion draft model.

tags LLM, Diffusion models, Language modeling, Transformers Language model architecture that use diffusion instead of autoregression.

tags LLM, Optimization, Test-time compute, Language modeling The principle of speculative decoding for LLMs is based on the asymmetry of Transformer-based architecture between: decoding tokens one by one, resulting in individual full passes through the mo…

tags Machine learning

tags Machine learning, LLM, Agent, Evaluating NLP

tags Agent, Coding agent, Multi-agent collaboration An open protocol (originally from Anthropic) that standardizes how AI agents connect to external tools and data sources.

tags Reinforcement learning, Distillation, Self-training Three early 2026 papers (MiniMax-M1 (CISPO), Zhang et al.

tags Logic

tags Reinforcement learning, Algorithm, Machine learning

tags Reinforcement learning, Algorithm, Machine learning

tags Foundation models, Reinforcement learning, Transformers, Scaling laws source (MiniMax 2025) Summary MiniMax-M1 is the first open-weight, large-scale reasoning model built on a hybrid attention architecture combining Transformers with lightning attent…

tags Transformers, LLM, Scaling laws, Attention, Residual neural networks source (Chen et al. 2026) Summary Standard residual connections in modern LLMs accumulate all layer outputs with fixed unit weights via PreNorm, causing uncontrolled hidden-state gr…

tags Transformers, RNN source (Katharopoulos et al. 2020) Summary Transformers have traditionally been described as different models from RNNs.

tags Attention, Transformers, Machine learning, Applied maths Attention variants that replace softmax with linear kernels, reducing complexity from quadratic to linear in sequence lengt

tags Transformers, LLM, Machine learning, Scaling laws Sparse neural network architecture that routes inputs to a subset of expert subnetworks, enabling parameter scaling without proportional compute increase.

tags Distillation, Language modeling, Program synthesis, Large language models source (Zhang et al. 2026) Summary This paper introduces simple self-distillation (SSD), a method where an LLM improves its own code generation by sampling solutions from itself

tags Machine learning, LLM, Reinforcement learning

tags Machine learning, Reinforcement learning, LLM This is related to RLHF, but instead of relying on human scoring of outputs, it uses programmatically verifiable outcomes (such as unit tests for code, math proofs, etc.).

tags Machine learning, Distillation, Language modeling, LLM Implications for open-ended evolution Looking at self training through the lense of Open-ended Evolution, it feels like pure self training (only a model with itself) cannot lead to radical improv…